Dendrites Enable a Robust Mechanism for Neuronal Stimulus Selectivity

Cazé, Romain D.; Jarvis, Sarah; Foust, Amanda J.; Schultz, Simon R.

doi:10.1162/neco_a_00989

Cited by 14 publications

(10 citation statements)

References 41 publications

(40 reference statements)

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“…Our results also relate to a study 21 where we made use of dendrites to implement another computation with relevance to biology, namely direction selectivity. In the earlier study, we demonstrated how dendrites make the implementation resilient to massive synaptic failure, and this past work shows that dendrites improve the computing robustness.…”

Section: Discussionsupporting

confidence: 57%

Dendritic neurons can perform linearly separable computations with low resolution synaptic weights

Cazé

Stimberg

2020

F1000Res

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In theory, neurons modelled as single layer perceptrons can implement all linearly separable computations. In practice, however, these computations may require arbitrarily precise synaptic weights. This is a strong constraint since both biological neurons and their artificial counterparts have to cope with limited precision. Here, we explore how non-linear processing in dendrites helps overcome this constraint. We start by finding a class of computations which requires increasing precision with the number of inputs in a Perceptron and show that it can be implemented without this constraint in a neuron with sub-linear dendritic subunits. Then, we complement this analytical study by a simulation of a biophysical neuron model with two passive dendrites and a soma, and show that it can implement this computation. This work demonstrates a new role of dendrites in neural computation: by distributing the computation across independent subunits, the same computation can be performed more efficiently with less precise tuning of the synaptic weights. This work not only offers new insight into the importance of dendrites for biological neurons, but also paves the way for new, more efficient architectures of artificial neuromorphic chips.

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Section: Discussionsupporting

confidence: 57%

Dendritic neurons can perform linearly separable computations with low resolution synaptic weights

Cazé

Stimberg

2020

F1000Res

Self Cite

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“…Our results also relate to a study [4] where we made use of dendrites to implement another computation with relevance to biology, namely direction selectivity. In the earlier study, we demonstrated how dendrites make the implementation resilient to massive synaptic failure, and this past work shows that dendrites improve the computing robustness.…”

Section: Discussionsupporting

confidence: 57%

Dendrites decrease the synaptic weight resolution necessary to implement linearly separable computations

Cazé

Stimberg

2020

Preprint

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In theory, neurons can compute all threshold functions, but in practice, synaptic weight resolution limits their computing capacity. Here, we study how dendrites alleviate this practical limitation by demonstrating a computation where dendrites considerably decrease the necessary synaptic weights resolution. We show how a biophysical neuron model with two passive dendrites and a soma that can implement this computation more efficiently than a point neuron. The latter requires synaptic weight orders of magnitudes larger than the others, the former implement the computation with equal synaptic weights. This work paves the way for a new generation of neuromorphic chips composed of dendritic neurons. These chips will require less space and less energy to function.

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“…This stimulus selectivity increases the neuron’s robustness to synaptic and dendritic failure. Using compartmental modeling they also showed how this clustered activation model is compatible with the that the mixture of selectivities in dendrites is different from the somatic selectivity (Cazé et al, 2017). Synaptic clustering may also have a functional role in the olfactory system.…”

Section: Synapse Clustering In Sensory Systemsmentioning

confidence: 79%

“…Nonlinear dendritic processing can affect the stimulus selectivity of a neuron, even if the total synaptic weight of the preferred stimulus does not exceed that of the non-preferred input. Cazé et al (2017) used computational modeling to demonstrate how this stimulus selectivity arises from the spatial distribution of synapses. This stimulus selectivity increases the neuron’s robustness to synaptic and dendritic failure.…”

Section: Synapse Clustering In Sensory Systemsmentioning

confidence: 99%

Synaptic Clustering and Memory Formation

Kastellakis

Poirazi

2019

Front. Mol. Neurosci.

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In the study of memory engrams, synaptic memory allocation is a newly emerged theme that focuses on how specific synapses are engaged in the storage of a given memory. Cumulating evidence from imaging and molecular experiments indicates that the recruitment of synapses that participate in the encoding and expression of memory is neither random nor uniform. A hallmark observation is the emergence of groups of synapses that share similar response properties and/or similar input properties and are located within a stretch of a dendritic branch. This grouping of synapses has been termed “synapse clustering” and has been shown to emerge in many different memory-related paradigms, as well as in in vitro studies. The clustering of synapses may emerge from synapses receiving similar input, or via many processes which allow for cross-talk between nearby synapses within a dendritic branch, leading to cooperative plasticity. Clustered synapses can act in concert to maximally exploit the nonlinear integration potential of the dendritic branches in which they reside. Their main contribution is to facilitate the induction of dendritic spikes and dendritic plateau potentials, which provide advanced computational and memory-related capabilities to dendrites and single neurons. This review focuses on recent evidence which investigates the role of synapse clustering in dendritic integration, sensory perception, learning, and memory as well as brain dysfunction. We also discuss recent theoretical work which explores the computational advantages provided by synapse clustering, leading to novel and revised theories of memory. As an eminent phenomenon during memory allocation, synapse clustering both shapes memory engrams and is also shaped by the parallel plasticity mechanisms upon which it relies.

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Dendrites Enable a Robust Mechanism for Neuronal Stimulus Selectivity

Cited by 14 publications

References 41 publications

Dendritic neurons can perform linearly separable computations with low resolution synaptic weights

Dendritic neurons can perform linearly separable computations with low resolution synaptic weights

Dendrites decrease the synaptic weight resolution necessary to implement linearly separable computations

Synaptic Clustering and Memory Formation

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